• Applications of machine learning methods to study a hydrodynamic field resulting from a simulation of an oscillatory turbulent internal flow.• Use of Python to employ Principal Component Analysis (PCA) to reduce the information necessary for describing the hydrodynamic field.• Adjustment of a nonlinear model to predict the temporal dynamics of the fluid velocity field by estimating the evolution of its principal components.

• Development of a Python code to analyze various configurations for positioning the thrusters of an autonomous underwater vehicle (AUV).• Verification of the feasibility of achieving 6 degrees of freedom for the robot and identification of the optimal configuration that minimizes the average current drawn by each thruster.

• Conducted direct numerical simulations using the package Channelflow to study turbulent internal flows subjected to an oscillatory pressure gradient.• Utilized the Unix interface to operate the Graham computing cluster of the Digital Research Alliance of Canada.• Analyzed data with Python using statistical and spectral methods.